An assessment has been completed on the sRedList platform for the species Dinoponera lucida on the 15/06/2023. All outputs from this assessment are reported in the .csv and .shp files that were downloaded at the end of the assessment and they can be pushed on SIS through SIS Connect. This report is here to detail how these results were obtained (e.g., which parameters were used on the sRedList platform), to provide context around results (e.g., saving the different maps created on the platform), and help communication among assessors and with reviewers.





Step 1: Distribution

You used the sRedList platform to create a distribution map of your species from occurrence records.

Download data

You gathered 59 raw occurrence records from GBIF + Uploaded . They are distributed as follows:


Filter data

To filter occurrence records, the platform offers some automated filters from the CoordinateCleanR package (see sRedList documentation for more information); you chose to apply those filters in your assessment. In addition, you excluded all records made before 2000 (also excluding records that do not have year information). You also excluded all records with coordinates uncertainty higher than 10km (but keeping records that do not have coordinate uncertainty information). You restricted to occurrence records included in a square of coordinates: -55,180,-90,90. You excluded occurrence records made at sea.

Occurrence records are displayed here (valid records in yellow and excluded records in purple; you can click on observation get more information and exclusion reason, check the sRedList documentation for more details):

After applying these filters, your point range map included 43 occurrence records.

Create range map

To create a polygon range map, you chose first to draw an alpha hull around occurrence records, fixing the alpha hull parameter to 0.2 (note that the parameter provided here differs from the alpha parameter per se as we scale it by the length of the diagonal of the geographic extent of the points (in meters) in order to have a reasonable range of values that work for species with different range sizes; the true unscaled alpha parameter is then displayed as a caption at the bottom of the plot).. You then buffered that polygon by 70 km. You excluded all the marine area from the polygon.

The range map created at this step is:

Smooth range map

You used the smoothing option to make polygon edges less sharp with a smoothing parameter of -1.

The final range map used in the next analyses is:




Step 2: Countries of occurrence

Countries of occurrence were extracted by overlapping the distribution saved in step 1 with a map of countries matching the list of countries and subnational entities from the Red List (see the sRedList documentation for further details). As you selected the species occurs in the system(s): Terrestrial, we used a terrestrial map of countries. The following map was obtained:

The list of countries of occurrence is:

Country

Subnational

Brazil

Bahia

Brazil

Espírito Santo

Brazil

Minas Gerais

Brazil

Rio de Janeiro


By default, we assumed for all countries the codes “Extant” for presence, “Native” for origin, “Resident” for seasonality. If you want to edit those, or the list of countries, you will have to do that manually in SIS. If you do not want to use this list at all, you can delete the ‘countries.csv’ file from the ZIP output.




Step 3: Extent of occurrence

The extent of occurrence was calculated as the area of the minimum convex polygon around the distribution saved in Step 1. If you are confident that the distribution represents the true range of your species (including inferred presences), you can use this estimate in criterion B1.


The extent of occurrence calculated by the platform was 187862km2. The final extent of occurrence (present in the downloaded output) has a value of 187862km2 and as justification :‘The EOO has been estimated as the Minimum Convex Polygon around the distribution on the sRedList platform.’.




Step 4: Area of Habitat

As you have drawn distribution maps from occurrence points, we calculate the species known area of occupancy based on these records. Note that this is a very conservative estimate of the area of occupancy as it is only based on known occurrences (or even just a sample if there were more than 2000 records in GBIF). The value of known area of occupancy is 120km2.


Habitat preferences

After a possibility to manually edit these, the habitat preferences used to map Area of Habitat were:

Habitat lookup

Habitat name

Suitability

1.6

Forest - Subtropical/Tropical Moist Lowland

Suitable

1.5

Forest - Subtropical/Tropical Dry

Suitable

14.5

Artificial/Terrestrial - Urban Areas

Unknown

Note that the major importance and seasonality fields are left empty from the habitats.csv file, so you might want to complete that information in SIS later.

Elevation preferences

Elevation preferences were suggested by the platform with a lower elevation limit of 1m and an upper elevation limit of 2619m (calculated as the minimum and maximum elevation within the species range). After a possibility to manually edit these values and add uncertainty in the estimates, the elevation preferences used to map Area of Habitat were 0, 1000-1600.

Density

You did not provide a density value and thus did not estimate population size.

AOH calculation

The calculated Area of Habitat has a value of 3.1159^{4} km2.


When rescaled at a 2x2km grid, this provides a value of 9.48^{4} km2; this value can be used (if you trust the map) as an upper bound of area of occupancy if you consider likely that all the mapped suitable habitat is occupied by the species. If you know part of it is not occupied, you might want to reduce this estimate. .





Step 6a: Fragmentation

Two parameters have to be provided to calculate fragmentation: the isolation distance (you provided the value 7 km) and the average density in mature individuals (you provided the value 4.5 inds/km2).

The fragmentation resulting map was:


Assuming that you trust the map of Area of Habitat and that mapped clusters correspond to isolated subpopulation, this plot means that you can consider the population severely fragmented if you consider that a subpopulation lower than 140788 mature individuals is small. You can check the sRedList documentation for interpretation.

If you trust that clusters represent isolated subpopulations, you can also use this analysis to inform subcriterion C2(a). We estimate that the largest subpopulation hosts 1.40788^{5} mature individuals (C2(ai)), representing 100% of the total population.




Step 7: Summary and assign categories

After the summary step, these are the parameters that are included in the final output allfields.csv:

Variable

Value

AOO.justification

The area of occupancy was estimated on the sRedList platform. Its lower bound (120km2) was estimated as the area of 2x2km grid cells intersecting with occurrence records (43 occurrence records were retrieved from GBIF (N=40), occurrence records uploaded by sRedList user (N=3)); this estimate assumes that the species range has been extensively surveyed at a 2x2km scale. Given that the species is largely undetected, it is very unlikely that the area of occupancy is lower than 2,000km2. The upper bound of area of occupancy (94800-103832km2) has been estimated by rescaling the map of Area of Habitat to a 2x2km grid; this estimates assumes that all suitable habitat is occupied by the species (at a 2x2km scale).

AOO.range

2000-103832

CurrentTrendDataDerivation.value

Inferred

EOO.justification

The EOO has been estimated as the Minimum Convex Polygon around the distribution on the sRedList platform.

EOO.range

187862

ElevationLower.limit

0

ElevationUpper.limit

1000-1600

GenerationLength.range

1

MatureIndividualsSubpopulation.value

100

MaxSubpopulationSize.range

140788

NoThreats.noThreats

false

PopulationReductionPast.direction

Increase

PopulationReductionPast.justification

This trend has been measured from the sRedList platform as the trend in Area of Habitat between 2010 and 2020. However trends in forest cover suggest habitat has declined in the past years.

PopulationReductionPast.range

4

SevereFragmentation.isFragmented

No

ThreatsUnknown.value

false

TrendInWildOfftake.value

Decreasing

internal_taxon_id

17491412213

internal_taxon_name

Dinoponera lucida



These parameters lead to the following Red List criteria application (note though that this is just for visualisation and will not be pushed to SIS):





Thank you for using sRedList! You can now either go to SIS and paste the parameters calculated here that you find interesting, or use the ZIP file to push your assessment to SIS Connect.